Comparative binding energy analysis of HIV-1 protease inhibitors: incorporation of solvent effects and validation as a powerful tool in receptor-based drug design

Characterization of the mechanism of bile salt hydrolase substrate specificity by experimental and computational analyses

Bile salt hydrolases (BSHs) are currently being investigated as target enzymes for metabolic regulators in humans and as growth promoters in farm animals. Understanding structural features underlying substrate specificity is necessary for inhibitor design. Here, we used a multidisciplinary workflow including mass spectrometry, mutagenesis, molecular dynamic simulations, machine learning, and crystallography to demonstrate substrate specificity in Lactobacillus salivarius BSH, the most abundant enzyme in human and farm animal intestines. We show the preference of substrates with a taurine head and a dehydroxylated sterol ring for hydrolysis. A regression model that correlates the relative rates of hydrolysis of various substrates in various enzyme mutants with the residue-substrate interaction energies guided the identification of structural determinants of substrate binding and specificity. In addition, we found T208 from another BSH protomer regulating the hydrolysis. The designed workflow can be used for fast and comprehensive characterization of enzymes with a broad range of substrates.

Ligand-based and structure-based studies to develop predictive models for SARS-CoV-2 main protease inhibitors through the 3d-qsar.com portal

The main protease (Mpro) of SARS-Cov-2 is the essential enzyme for maturation of functional proteins implicated in viral replication and transcription. The peculiarity of its specific cleavage site joint with its high degree of conservation among all coronaviruses promote it as an attractive target to develop broad-spectrum inhibitors, with high selectivity and tolerable safety profile. Herein is reported a combination of three-dimensional quantitative structure-activity relationships (3-D QSAR) and comparative molecular binding energy (COMBINE) analysis to build robust and predictive ligand-based and structure-based statistical models, respectively. Models were trained on experimental binding poses of co-crystallized Mpro-inhibitors and validated on available literature data. By means of deep optimization both models' goodness and robustness reached final statistical values of r2/q2 values of 0.97/0.79 and 0.93/0.79 for the 3-D QSAR and COMBINE approaches respectively, and an overall predictiveness values of 0.68 and 0.57 for the SDEPPRED and AAEP metrics after application to a test set of 60 compounds covered by the training set applicability domain. Despite the different nature (ligand-based and structure-based) of the employed methods, their outcome fully converged. Furthermore, joint ligand- and structure-based structure-activity relationships were found in good agreement with nirmatrelvir chemical features properties, a novel oral Mpro-inhibitor that has recently received U.S. FDA emergency use authorization (EUA) for the oral treatment of mild-to-moderate COVID-19 infected patients. The obtained results will guide future rational design and/or virtual screening campaigns with the aim of discovering new potential anti-coronavirus lead candidates, minimizing both time and financial resources. Moreover, as most of calculation were performed through the well-established web portal 3d-qsar.com the results confirm the portal as a useful tool for drug design.

Prediction of the Drug-Target Binding Kinetics for Flexible Proteins by Comparative Binding Energy Analysis

There is growing consensus that the optimization of the kinetic parameters for drug-protein binding leads to improved drug efficacy. Therefore, computational methods have been developed to predict kinetic rates and to derive quantitative structure-kinetic relationships (QSKRs). Many of these methods are based on crystal structures of ligand-protein complexes. However, a drawback is that each ligand-protein complex is usually treated as having a single structure. Here, we present a modification of COMparative BINding Energy (COMBINE) analysis, which uses the structures of ligand-protein complexes to predict binding parameters. We introduce the option of using multiple structures to describe each ligand-protein complex in COMBINE analysis and apply this to study the effects of protein flexibility on the derivation of dissociation rate constants (k_off) for inhibitors of p38 mitogen-activated protein (MAP) kinase, which has a flexible binding site. Multiple structures were obtained for each ligand-protein complex by performing docking to an ensemble of protein configurations obtained from molecular dynamics simulations. Coefficients to scale ligand-protein interaction energies determined from energy-minimized structures of ligand-protein complexes were obtained by partial least squares regression, and they allowed for the computation of k_off values. The QSKR model obtained using single, energy-minimized crystal structures for each ligand-protein complex had higher predictive power than the QSKR model obtained with multiple structures from ensemble docking. However, incorporation of ligand-protein flexibility helped to highlight additional ligand-protein interactions that lead to longer residence times, such as interactions with residues Arg67 and Asp168, which are close to the ligand in many crystal structures. These results show that COMBINE analysis is a promising method to guide the design of compounds that bind to flexible proteins with improved binding kinetics.

Machine Learning Analysis of τRAMD Trajectories to Decipher Molecular Determinants of Drug-Target Residence Times

Drug-target residence times can impact drug efficacy and safety, and are therefore increasingly being considered during lead optimization. For this purpose, computational methods to predict residence times, τ, for drug-like compounds and to derive structure-kinetic relationships are desirable. A challenge for approaches based on molecular dynamics (MD) simulation is the fact that drug residence times are typically orders of magnitude longer than computationally feasible simulation times. Therefore, enhanced sampling methods are required. We recently reported one such approach: the τRAMD procedure for estimating relative residence times by performing a large number of random acceleration MD (RAMD) simulations in which ligand dissociation occurs in times of about a nanosecond due to the application of an additional randomly oriented force to the ligand. The length of the RAMD simulations is used to deduce τ. The RAMD simulations also provide information on ligand egress pathways and dissociation mechanisms. Here, we describe a machine learning approach to systematically analyze protein-ligand binding contacts in the RAMD trajectories in order to derive regression models for estimating τ and to decipher the molecular features leading to longer τ values. We demonstrate that the regression models built on the protein-ligand interaction fingerprints of the dissociation trajectories result in robust estimates of τ for a set of 94 drug-like inhibitors of heat shock protein 90 (HSP90), even for the compounds for which the length of the RAMD trajectories does not provide a good estimation of τ. Thus, we find that machine learning helps to overcome inaccuracies in the modeling of protein-ligand complexes due to incomplete sampling or force field deficiencies. Moreover, the approach facilitates the identification of features important for residence time. In particular, we observed that interactions of the ligand with the sidechain of F138, which is located on the border between the ATP binding pocket and a hydrophobic transient sub-pocket, play a key role in slowing compound dissociation. We expect that the combination of the τRAMD simulation procedure with machine learning analysis will be generally applicable as an aid to target-based lead optimization.

Active learning strategies with COMBINE analysis: new tricks for an old dog

The COMBINE method was designed to study congeneric series of compounds including structural information of ligand-protein complexes. Although very successful, the method has not received the same level of attention than other alternatives to study Quantitative Structure Active Relationships (QSAR) mainly because lack of ways to measure the uncertainty of the predictions and the need for large datasets. Active learning, a semi-supervised learning approach that makes use of uncertainty to enhance models' performance while reducing the size of the training sets, has been used in this work to address both problems. We propose two estimators of uncertainty: the pool of regressors and the distance to the training set. The performance of the methods has been evaluated by testing the resulting active learning workflows in 3 diverse datasets: HIV-1 protease inhibitors, Taxol-derivatives and BRD4 inhibitors. The proposed strategies were successful in 80% of the cases for the taxol-derivatives and BRD4 inhibitors, while outperformed random selection in the case of the HIV-1 protease inhibitors time-split. Our results suggest that AL-COMBINE might be an effective way of producing consistently superior QSAR models with a limited number of samples.

Prediction of Drug-Target Binding Kinetics by Comparative Binding Energy Analysis

A growing consensus is emerging that optimizing the drug–target affinity alone under equilibrium conditions does not necessarily translate into higher potency in vivo and that instead binding kinetic parameters should be optimized to ensure better efficacy. Therefore, in silico methods are needed to predict the kinetic parameters and the mechanistic determinants of drug–protein binding. Here we demonstrate the application of COMparative BINding Energy (COMBINE) analysis to derive quantitative structure–kinetics relationships (QSKRs) for the dissociation rate constants (k_off) of inhibitors of heat shock protein 90 (HSP90) and HIV-1 protease. We derived protein-specific scoring functions by correlating k_off rate constants with a subset of weighted interaction energy components determined from the energy-minimized structures of drug–protein complexes. As the QSKRs derived for these sets of chemically diverse compounds have good predictive ability and provide insights into important drug–protein interactions for optimizing k_off, COMBINE analysis offers a promising approach for binding kinetics-guided lead optimization.

Finding the structural requirements of diverse HIV-1 protease inhibitors using multiple QSAR modelling for lead identification

Multiple Quantitative Structure-Activity Relationship (QSAR) analysis is widely used in drug discovery for lead identification. Human Immunodeficiency Virus (HIV) protease is one of the key targets for the treatment of Acquired Immunodeficiency Syndrome (AIDS). One of the major challenges for the design of HIV-1 protease inhibitors (HIV PRIs) is to increase the inhibitory activities against the enzyme to a level where the problem associated to drug resistance may be considerably delayed. Herein, chemometric analyses were performed with 346 structurally diverse HIV PRIs with experimental bioactivities against a sub-type B mutant to develop highly predictable QSAR models and also to identify the effective structural determinants for higher affinity against HIV PR. The QSAR models were developed using OCHEM-based machine learning tools (ASNN, FSMLR, KNN, RF, MANN and XGBoost), with descriptors calculated by eight different software packages. Simultaneously, a Monte Carlo optimization-based QSAR modelling was performed using SMILES and graph-based descriptors to understand fragment and topochemical contributions. To validate the actual predictability of all these models, an additional set of 104 compounds (also with known experimental activities) with slightly different chemical space were employed. This ligand-based study serves as a crucial benchmark for further development of the HIV protease inhibitors with improved activities.

Facile access to functionalized indenes and fused quinolines by regioselective 5-enolexo-dig Michael addition and cyclization reactions

Herein we report a facile approach to synthesise multi-substituted indenes and cyclopenta[b]quinolines under mild conditions.

Structural basis for the development of SARS 3CL protease inhibitors from a peptide mimic to an aza-decaline scaffold

Design of inhibitors against severe acute respiratory syndrome (SARS) chymotrypsin-like protease (3CL(pro) ) is a potentially important approach to fight against SARS. We have developed several synthetic inhibitors by structure-based drug design. In this report, we reveal two crystal structures of SARS 3CL(pro) complexed with two new inhibitors based on our previous work. These structures combined with six crystal structures complexed with a series of related ligands reported by us are collectively analyzed. To these eight complexes, the structural basis for inhibitor binding was analyzed by the COMBINE method, which is a chemometrical analysis optimized for the protein-ligand complex. The analysis revealed that the first two latent variables gave a cumulative contribution ratio of r(2)  = 0.971. Interestingly, scores using the second latent variables for each complex were strongly correlated with root mean square deviations (RMSDs) of side-chain heavy atoms of Met(49) from those of the intact crystal structure of SARS-3CL(pro) (r = 0.77) enlarging the S2 pocket. The substantial contribution of this side chain (∼10%) for the explanation of pIC50 s was dependent on stereochemistry and the chemical structure of the ligand adapted to the S2 pocket of the protease. Thus, starting from a substrate mimic inhibitor, a design for a central scaffold for a low molecular weight inhibitor was evaluated to develop a further potent inhibitor. © 2015 Wiley Periodicals, Inc. Biopolymers (Pept Sci) 106: 391-403, 2016.

Prioritization of active antimalarials using structural interaction profile of Plasmodium falciparum enoyl-acyl carrier protein reductase (PfENR)-triclosan derivatives

An empirical relationship between the experimental inhibitory activities of triclosan derivatives and its computationally predicted Plasmodium falciparum enoyl-acyl carrier protein (ACP) reductase (PfENR) dock poses was developed to model activities of known antimalarials. A statistical model was developed using 57 triclosan derivatives with significant measures (r = 0.849, q(2) = 0.619, s = 0.481) and applied on structurally related and structurally diverse external datasets. A substructure-based search on ChEMBL malaria dataset (280 compounds) yielded only two molecules with significant docking energy, whereas eight active antimalarials (EC(50) < 100 nM, tested on 3D7 strain) with better predicted activities (pIC(50) ~ 7) from Open Access Malaria Box (400 compounds) were prioritized. Further, calculations on the structurally diverse rhodanine molecules (known PfENR inhibitors) distinguished actives (experimental IC(50) = 0.035 μM; predicted pIC(50) = 6.568) and inactives (experimental IC(50) = 50 μM; predicted pIC50 = -4.078), which showed that antimalarials possessing dock poses similar to experimental interaction profiles can be used as leads to test experimentally on enzyme assays.

Molecular recognition of epothilones by microtubules and tubulin dimers revealed by biochemical and NMR approaches

The binding of epothilones to dimeric tubulin and to microtubules has been studied by means of biochemical and NMR techniques. We have determined the binding constants of epothilone A (EpoA) and B (EpoB) to dimeric tubulin, which are 4 orders of magnitude lower than those for microtubules, and we have elucidated the conformation and binding epitopes of EpoA and EpoB when bound to tubulin dimers and microtubules in solution. The determined conformation of epothilones when bound to dimeric tubulin is similar to that found by X-ray crystallographic techniques for the binding of EpoA to the Tubulin/RB3/TTL complex; it is markedly different from that reported for EpoA bound to zinc-induced sheets obtained by electron crystallography. Likewise, only the X-ray structure of EpoA bound to the Tubulin/RB3/TTL complex at the luminal site, but not the electron crystallography structure, is compatible with the results obtained by STD on the binding epitope of EpoA bound to dimeric tubulin, thus confirming that the allosteric change (structuring of the M-loop) is the biochemical mechanism of induction of tubulin assembly by epothilones. TR-NOESY signals of EpoA bound to microtubules have been obtained, supporting the interaction with a transient binding site with a fast exchange rate (pore site), consistent with the notion that epothilones access the luminal site through the pore site, as has also been observed for taxanes. Finally, the differences in the tubulin binding affinities of a series of epothilone analogues has been quantitatively explained using the newly determined binding pose and the COMBINE methodology.

Directed modification of the Aspergillus usamii β-mannanase to improve its substrate affinity by in silico design and site-directed mutagenesis

β-Mannanases (EC 3.2.1.78) can catalyze the cleavage of internal β-1,4-d-mannosidic linkages of mannan backbones, and they have found applications in food, feed, pulp and paper, oil, pharmaceutical and textile industries. Suitable amino acid substitution can promote access to the substrate-binding groove and maintain the substrate therein, which probably improves the substrate affinity and, thus, increases catalytic efficiency of the enzyme. In this study, to improve the substrate affinity of AuMan5A, a glycoside hydrolase (GH) family 5 β-mannanase from Aspergillus usamii, had its directed modification conducted by in silico design, and followed by site-directed mutagenesis. The mutant genes, Auman5AY111F and Auman5AY115F, were constructed by megaprimer PCR, respectively. Then, Auman5A and its mutant genes were expressed in Pichia pastoris GS115 successfully. The specific activities of purified recombinant β-mannanases (reAuMan5A, reAuMan5AY111F and reAuMan5AY115F) towards locust bean gum were 152.5, 199.6 and 218.9 U mg−1, respectively. The two mutants were found to be similar to reAuMan5A regarding temperature and pH characteristics. Nevertheless, the K  m values of reAuMan5AY111F and reAuMan5AY115F, towards guar gum, decreased to 2.95 ± 0.22 and 2.39 ± 0.33 mg ml−1 from 4.49 ± 0.07 mg ml−1 of reAuMan5A, which would make reAuMan5AY111F and reAuMan5AY115F promising candidates for industrial processes. Structural analysis showed that the two mutants increased their affinity by decreasing the steric conflicts with those more complicated substrates. The results suggested that subtle conformational modification in the substrate-binding groove could substantially alter the substrate affinity of AuMan5A. This study laid a solid foundation for the directed modification of substrate affinities of β-mannanases and other enzymes.

Pharmacophore-based screening and identification of novel human ligase I inhibitors with potential anticancer activity

Human DNA ligases are enzymes that are indispensable for DNA replication and repair processes. Among the three human ligases, ligase I is attributed to the ligation of thousands of Okazaki fragments that are formed during lagging strand synthesis during DNA replication. Blocking ligation therefore can lead to the accumulation of thousands of single strands and subsequently double strand breaks in the DNA, which is lethal for the cells. The reports of the high expression level of ligase I protein in several cancer cells (versus the low ligase expression level and the low rate of division of most normal cells in the adult body) support the belief that ligase I inhibitors can target cancer cells specifically with minimum side effects to normal cells. Recent publications showing exciting data for a ligase IV inhibitor exhibiting antitumor activity in mouse models also strengthens the argument for ligases as valid antitumor targets. Keeping this in view, we performed a pharmacophore-based screening for potential ligase inhibitors in the Maybridge small molecule library and procured some of the top-ranking compounds for enzyme-based and cell-based in vitro screening. We report here the identification of novel ligase I inhibitors with potential anticancer activity against a colon cancer cell line.

A structure-based design of new C2- and C13-substituted taxanes: tubulin binding affinities and extended quantitative structure-activity relationships using comparative binding energy (COMBINE) analysis

Ten novel taxanes bearing modifications at the C2 and C13 positions of the baccatin core have been synthesized and their binding affinities for mammalian tubulin have been experimentally measured. The design strategy was guided by (i) calculation of interaction energy maps with carbon, nitrogen and oxygen probes within the taxane-binding site of β-tubulin, and (ii) the prospective use of a structure-based QSAR (COMBINE) model derived from an earlier series comprising 47 congeneric taxanes. The tubulin-binding affinity displayed by one of the new compounds (CTX63) proved to be higher than that of docetaxel, and an updated COMBINE model provided a good correlation between the experimental binding free energies and a set of weighted residue-based ligand-receptor interaction energies for 54 out of the 57 compounds studied. The remaining three outliers from the original training series have in common a large unfavourable entropic contribution to the binding free energy that we attribute to taxane preorganization in aqueous solution in a conformation different from that compatible with tubulin binding. Support for this proposal was obtained from solution NMR experiments and molecular dynamics simulations in explicit water. Our results shed additional light on the determinants of tubulin-binding affinity for this important class of antitumour agents and pave the way for further rational structural modifications.

Conformational analysis and intramolecular hydrogen bonding of cis-3-aminoindan-1-ol: a quantum chemical study

In the present work, we carried out a conformational analysis of cis-3-aminoindan-1-ol and evaluated the role of the intramolecular hydrogen bond in the stabilization of various conformers using quantum mechanical DFT (B3LYP) and MP2 methods. On the basis of relative energies, we have found nine conformational minima, which can interchange through the ring-puckering and the internal rotation of the OH and NH2 groups on the five-membered ring. The intramolecular hydrogen bonds such as OH∙∙∙∙π, NH∙∙∙∙π, NH∙∙∙∙OH and HN∙∙∙∙HO are expected to be of critical importance for the conformational stabilities. The intramolecular interactions of the minima have been analyzed by calculation of electron density (ρ) and Laplacian (ρ) at the bond critical points (BCPs) using atoms-in-molecule (AIM) theory. The existence or absence of OH∙∙∙∙π and NH∙∙∙∙π in cis-3-aminoindan-1-ol remains unclear since the geometrical investigation has not been confirmed by topological criteria. The results of theoretical calculations demonstrate that this compound exists predominantly in one ring-puckering form stabilized by strong hydrogen bond HN∙∙∙∙HO Interaction.

Fusing a carbohydrate-binding module into the Aspergillus usamii β-mannanase to improve its thermostability and cellulose-binding capacity by in silico design

The AuMan5A, an acidophilic glycoside hydrolase (GH) family 5 β-mannanase derived from Aspergillus usamii YL-01-78, consists of an only catalytic domain (CD). To perfect enzymatic properties of the AuMan5A, a family 1 carbohydrate-binding module (CBM) of the Trichoderma reesei cellobiohydrolase I (TrCBH I), having the lowest binding free energy with cellobiose, was selected by in silico design, and fused into its C-terminus forming a fusion β-mannanase, designated as AuMan5A-CBM. Then, its encoding gene, Auman5A-cbm, was constructed as it was designed theoretically, and expressed in Pichia pastoris GS115. SDS-PAGE analysis displayed that both recombinant AuMan5A-CBM (reAuMan5A-CBM) and AuMan5A (reAuMan5A) were secreted into the cultured media with apparent molecular masses of 57.3 and 49.8 kDa, respectively. The temperature optimum of the reAuMan5A-CBM was 75°C, being 5°C higher than that of the reAuMan5A. They were stable at temperatures of 68 and 60°C, respectively. Compared with reAuMan5A, the reAuMan5A-CBM showed an obvious decrease in K_m and a slight alteration in V_max. In addition, the fusion of a CBM of the TrCBH I into the AuMan5A contributed to its cellulose-binding capacity.

Improved estimation of protein-ligand binding free energy by using the ligand-entropy and mobility of water molecules

We previously developed the direct interaction approximation (DIA) method to estimate the protein-ligand binding free energy (DG). The DIA method estimates the DG value based on the direct van der Waals and electrostatic interaction energies between the protein and the ligand. In the current study, the effect of the entropy of the ligand was introduced with protein dynamic properties by molecular dynamics simulations, and the interaction between each residue of the protein and the ligand was also weighted considering the hydration of each residue. The molecular dynamics simulation of the apo target protein gave the hydration effect of each residue, under the assumption that the residues, which strongly bind the water molecules, are important in the protein-ligand binding. These two effects improved the reliability of the DIA method. In fact, the parameters used in the DIA became independent of the target protein. The averaged error of DG estimation was 1.3 kcal/mol and the correlation coefficient between the experimental DG value and the calculated DG value was 0.75.

Comparative binding energy COMBINE analysis for understanding the binding determinants of type II dehydroquinase inhibitors

Herein we report comparative binding energy (COMBINE) analyses to derive quantitative structure-activity relationship (QSAR) models that help rationalize the determinants of binding affinity for inhibitors of type II dehydroquinase (DHQ2), the third enzyme of the shikimic acid pathway. Independent COMBINE models were derived for Helicobacter pylori and Mycobacterium tuberculosis DHQ2, which is an essential enzyme in both these pathogenic bacteria that has no counterpart in human cells. These studies quantify the importance of the hydrogen bonding interactions between the ligands and the water molecule involved in the DHQ2 reaction mechanism. They also highlight important differences in the ligand interactions with the interface pocket close to the active site that could provide guides for future inhibitor design.

Limiting assumptions in molecular modeling: electrostatics

Molecular mechanics attempts to represent intermolecular interactions in terms of classical physics. Initial efforts assumed a point charge located at the atom center and coulombic interactions. It is been recognized over multiple decades that simply representing electrostatics with a charge on each atom failed to reproduce the electrostatic potential surrounding a molecule as estimated by quantum mechanics. Molecular orbitals are not spherically symmetrical, an implicit assumption of monopole electrostatics. This perspective reviews recent evidence that requires use of multipole electrostatics and polarizability in molecular modeling.

Molecular simulations of drug–receptor complexes in anticancer research

Molecular modeling and computer simulation techniques have matured significantly in recent years and proved their value in the study of drug–DNA, drug–DNA–protein, drug–protein and protein–protein interactions. Evolution in this area has gone hand-in-hand with an increased availability of structural data on biological macromolecules, major advances in molecular mechanics force fields and considerable improvements in computer technologies, most significantly processing speeds, multiprocessor programming and data-storage capacity. The information derived from molecular simulations of drug–receptor complexes can be used to extract structural and energetic information that is usually beyond current experimental possibilities, provide independent accounts of experimentally observed behavior, help in the interpretation of biochemical or pharmacological results, and open new avenues for research by posing novel relevant questions that can guide the design of new experiments. As drug-screening tools, ligand- and fragment-docking platforms stand out as powerful techniques that can provide candidate molecules for hit and lead development. This review provides an overall perspective of the main methods and focuses on some selected applications to both classical and novel anticancer targets.

Statistical estimation of the protein-ligand binding free energy based on direct protein-ligand interaction obtained by molecular dynamics simulation

We have developed a method for estimating protein-ligand binding free energy (DG) based on the direct protein-ligand interaction obtained by a molecular dynamics simulation. Using this method, we estimated the DG value statistically by the average values of the van der Waals and electrostatic interactions between each amino acid of the target protein and the ligand molecule. In addition, we introduced fluctuations in the accessible surface area (ASA) and dihedral angles of the protein-ligand complex system as the entropy terms of the DG estimation. The present method included the fluctuation term of structural change of the protein and the effective dielectric constant. We applied this method to 34 protein-ligand complex structures. As a result, the correlation coefficient between the experimental and calculated DG values was 0.81, and the average error of DG was 1.2 kcal/mol with the use of the fixed parameters. These results were obtained from a 2 nsec molecular dynamics simulation.

Exploring the binding of BACE-1 inhibitors using comparative binding energy analysis (COMBINE)

BACKGROUND

The inhibition of the activity of β-secretase (BACE-1) is a potentially important approach for the treatment of Alzheimer disease. To explore the mechanism of inhibition, we describe the use of 46 X-ray crystallographic BACE-1/inhibitor complexes to derive quantitative structure-activity relationship (QSAR) models. The inhibitors were aligned by superimposing 46 X-ray crystallographic BACE-1/inhibitor complexes, and gCOMBINE software was used to perform COMparative BINding Energy (COMBINE) analysis on these 46 minimized BACE-1/inhibitor complexes. The major advantage of the COMBINE analysis is that it can quantitatively extract key residues involved in binding the ligand and identify the nature of the interactions between the ligand and receptor.

RESULTS

By considering the contributions of the protein residues to the electrostatic and van der Waals intermolecular interaction energies, two predictive and robust COMBINE models were developed: (i) the 3-PC distance-dependent dielectric constant model (built from a single X-ray crystal structure) with a q2 value of 0.74 and an SDEC value of 0.521; and (ii) the 5-PC sigmoidal electrostatic model (built from the actual complexes present in the Brookhaven Protein Data Bank) with a q2 value of 0.79 and an SDEC value of 0.41.

CONCLUSIONS

These QSAR models and the information describing the inhibition provide useful insights into the design of novel inhibitors via the optimization of the interactions between ligands and those key residues of BACE-1.

Application of Machine Leaning in Drug Discovery and Development

Machine learning techniques have been widely used in drug discovery and development, particularly in the areas of cheminformatics, bioinformatics and other types of pharmaceutical research. It has been demonstrated they are suitable for large high dimensional data, and the models built with these methods can be used for robust external predictions. However, various problems and challenges still exist, and new approaches are in great need. In this Chapter, the authors will review the current development of machine learning techniques, and especially focus on several machine learning techniques they developed as well as their application to model building, lead discovery via virtual screening, integration with molecular docking, and prediction of off-target properties. The authors will suggest some potential different avenues to unify different disciplines, such as cheminformatics, bioinformatics and systems biology, for the purpose of developing integrated in silico drug discovery and development approaches.

Selective human adenosine A3 antagonists based on pyrido[2,1-f]purine-2,4-diones: novel features of hA3 antagonist binding

Based on our previous results on the potent antagonist effect of 1H,3H-pyrido[2,1-f]purine-2,4-diones at the human A(3) adenosine receptor, new series of this family of compounds have been synthesized and evaluated in radioligand binding studies against the human A(1), A(2A), A(2B), and A(3) receptors. A remarkable improvement in potency, and most noticeable, in selectivity has been achieved, as exemplified by the 3-cyclopropylmethyl-8-methoxy-1-(4-methylbenzyl)-1H,3H-pyrido[2,1-f]purine-2,4-dione (10) that combines a very high affinity at hA(3) (K(i)=2.24 nM), with lack of affinity for the A(1), A(2A), and A(2B) receptors. On the basis of the published hA(3) receptor model (PDB 1OEA), molecular modeling studies, including molecular dynamics (MD) simulations, have been performed to depict the binding mode of the 1 H,3H-pyrido[2,1-f]purine-2,4-diones and to justify the selectivity against the other adenosine receptors. These studies have led to novel features of the cavity where our antagonists are bound so that the cavity is lined by the hydrogen-bonded Gln 167-Asn 250 pair and by the highly conserved Phe 168.

Docking and multivariate methods to explore HIV-1 drug-resistance: a comparative analysis

In this paper we describe a comparative analysis between multivariate and docking methods in the study of the drug resistance to the reverse transcriptase and the protease inhibitors. In our early papers we developed a simple but efficient method to evaluate the features of compounds that are less likely to trigger resistance or are effective against mutant HIV strains, using the multivariate statistical procedures PCA and DA. In the attempt to create a more solid background for the prediction of susceptibility or resistance, we carried out a comparative analysis between our previous multivariate approach and molecular docking study. The intent of this paper is not only to find further support to the results obtained by the combined use of PCA and DA, but also to evidence the structural features, in terms of molecular descriptors, similarity, and energetic contributions, derived from docking, which can account for the arising of drug-resistance against mutant strains.

Antitumor Activity, X-ray Crystal Structure, and DNA Binding Properties of Thiocoraline A, a Natural Bisintercalating Thiodepsipeptide

The marine natural product thiocoraline A displayed approximately equal cytotoxic activity at nanomolar concentrations in a panel of 12 human cancer cell lines. X-ray diffraction analyses of orthorhombic crystals of this DNA-binding drug revealed arrays of docked pairs of staple-shaped molecules in which one pendent hydroxyquinoline chromophore from each cysteine-rich molecule appears intercalated between the two chromophores of a facing molecule. This arrangement is in contrast to the proposed mode of binding to DNA that shows the two drug chromophores clamping two stacked base pairs, in agreement with the nearest-neighbor exclusion principle. Proof of DNA sequence recognition was obtained from both classical DNase I footprinting experiments and determination of the melting temperatures of several custom-designed fluorescently labeled oligonucleotides. A rationale for the DNA-binding behavior was gained when models of thiocoraline clamping a central step embedded in several octanucleotides were built and studied by means of unrestrained molecular dynamics simulations in aqueous solution.

EADock: docking of small molecules into protein active sites with a multiobjective evolutionary optimization

In recent years, protein-ligand docking has become a powerful tool for drug development. Although several approaches suitable for high throughput screening are available, there is a need for methods able to identify binding modes with high accuracy. This accuracy is essential to reliably compute the binding free energy of the ligand. Such methods are needed when the binding mode of lead compounds is not determined experimentally but is needed for structure-based lead optimization. We present here a new docking software, called EADock, that aims at this goal. It uses an hybrid evolutionary algorithm with two fitness functions, in combination with a sophisticated management of the diversity. EADock is interfaced with the CHARMM package for energy calculations and coordinate handling. A validation was carried out on 37 crystallized protein-ligand complexes featuring 11 different proteins. The search space was defined as a sphere of 15 A around the center of mass of the ligand position in the crystal structure, and on the contrary to other benchmarks, our algorithm was fed with optimized ligand positions up to 10 A root mean square deviation (RMSD) from the crystal structure, excluding the latter. This validation illustrates the efficiency of our sampling strategy, as correct binding modes, defined by a RMSD to the crystal structure lower than 2 A, were identified and ranked first for 68% of the complexes. The success rate increases to 78% when considering the five best ranked clusters, and 92% when all clusters present in the last generation are taken into account. Most failures could be explained by the presence of crystal contacts in the experimental structure. Finally, the ability of EADock to accurately predict binding modes on a real application was illustrated by the successful docking of the RGD cyclic pentapeptide on the alphaVbeta3 integrin, starting far away from the binding pocket.

COMBINE analysis of the specificity of binding of Ras proteins to their effectors

The small guanosine triphosphate (GTP)-binding proteins of the Ras family are involved in many cellular pathways leading to cell growth, differentiation, and apoptosis. Understanding the interaction of Ras with other proteins is of importance not only for studying signalling mechanisms but also, because of their medical relevance as targets, for anticancer therapy. To study their selectivity and specificity, which are essential to their signal transfer function, we performed COMparative BINding Energy (COMBINE) analysis for 122 different wild-type and mutant complexes between the Ras proteins, Ras and Rap, and their effectors, Raf and RalGDS. The COMBINE models highlighted the amino acid residues responsible for subtle differences in binding of the same effector to the two different Ras proteins, as well as more significant differences in the binding of the two different effectors (RalGDS and Raf) to Ras. The study revealed that E37, D38, and D57 in Ras are nonspecific hot spots at its effector interface, important for stabilization of both the RalGDS-Ras and Raf-Ras complexes. The electrostatic interaction between a GTP analogue and the effector, either Raf or RalGDS, also stabilizes these complexes. The Raf-Ras complexes are specifically stabilized by S39, Y40, and D54, and RalGDS-Ras complexes by E31 and D33. Binding of a small molecule in the vicinity of one of these groups of amino acid residues could increase discrimination between the Raf-Ras and RalGDS-Ras complexes. Despite the different size of the RalGDS-Ras and Raf-Ras complexes, we succeeded in building COMBINE models for one type of complex that were also predictive for the other type of protein complex. Further, using system-specific models trained with only five complexes selected according to the results of principal component analysis, we were able to predict binding affinities for the other mutants of the particular Ras-effector complex. As the COMBINE analysis method is able to explicitly reveal the amino acid residues that have most influence on binding affinity, it is a valuable aid for protein design.

A quantitative structure-activity relationship study for structurally diverse HIV-1 protease inhibitors: Contribution of conformational flexibility to inhibitory activity

In this study, we investigated by linear regression model the SAR data of the 15 HIV-1 protease inhibitors possessing structurally diverse scaffolds. First, a regression model was developed only using the enzyme-inhibitor interaction energy as a term of the model, but did not provide a good correlation with the inhibitory activity (R2 = 0.580 and Q2 = 0.500). Then, we focused on the conformational flexibility of the inhibitors which may represent the diversity of the inhibitors, and added two conformational parameters into the model, respectively: the number of rotatable bonds of ligands (deltaSrot) and the distortion energy of ligands (deltaElig). The regression model by adding deltaElig successfully improved the quality of the model (R2 = 0.771 and Q2 = 0.713) while the model with deltaSrot was unsuccessful. The prediction for a training inhibitor by the deltaElig model also showed good agreement with experimental activity. These results suggest that the conformational flexibility of HIV-1 protease inhibitors directly contributes to the enzyme inhibition.

A novel automated lazy learning QSAR (ALL-QSAR) approach: method development, applications, and virtual screening of chemical databases using validated ALL-QSAR models

A novel automated lazy learning quantitative structure-activity relationship (ALL-QSAR) modeling approach has been developed on the basis of the lazy learning theory. The activity of a test compound is predicted from a locally weighted linear regression model using chemical descriptors and the biological activity of the training set compounds most chemically similar to this test compound. The weights with which training set compounds are included in the regression depend on the similarity of those compounds to a test compound. We have applied the ALL-QSAR method to several experimental chemical data sets including 48 anticonvulsant agents with known ED50 values, 48 dopamine D1-receptor antagonists with known competitive binding affinities (Ki), and a Tetrahymena pyriformis data set containing 250 phenolic compounds with toxicity IGC50 values. When applied to database screening, models developed for anticonvulsant agents identified several known anticonvulsant compounds that were not only absent in the training set but highly chemically dissimilar to the training set compounds. This initial success indicates that ALL-QSAR can be further exploited as a general tool for accurate bioactivity prediction and database screening in drug design and discovery. Because of its local nature, the ALL-QSAR approach appears to be especially well-suited for the development of highly predictive models for the sparse or unevenly distributed data sets.

Binding affinity prediction of non-peptide inhibitors of HIV-1 protease using COMBINE model introduced from peptide inhibitors

Comparative binding energy (COMBINE) analysis method is one of the QSAR techniques for the prediction of biological activities of inhibitors based on interaction energies between ligands and proteins decomposed into each amino acid residue. We supposed that the predictive ability of the COMBINE method does not depend essentially on the molecular frameworks of ligands. To verify this idea, we performed the COMBINE analysis of non-peptide inhibitors of HIV-1 protease (HIVp), where the prediction model was constructed using inhibitors with a peptide scaffold as a training set. The predictive performance of the AMBER and CHARMm force fields was very high and at the same level (q(2)=0.75, 0.67, SDEP(cv)=0.76, 0.89, and SDEP(ex)=0.92, 0.66, respectively). The high predictive ability of the COMBINE method for the distinct scaffold compounds is due to the informative description of the interaction energies for compounds that are located at the binding site. This result suggests that COMBINE analysis may be applied not only to the lead optimization stage but also to the lead evolution stages.